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Eric A. Rosenberg, Andrew W. Wood, and Anne C. Steinemann

needs are least satisfied and identify sites with the best potential to offer skill improvements. This paper presents a hydrometric network design approach toward the objective of enhancing statistical prediction models. The specific focus of the paper is the development of a forecast skill-oriented technique for informing NRCS SNOTEL network expansion decisions. We employ a hybrid dynamical–statistical approach that combines the dimension-reducing power of the NRCS PCR methodology with the

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Tirthankar Roy, Xiaogang He, Peirong Lin, Hylke E. Beck, Christopher Castro, and Eric F. Wood

feedbacks, improper model calibration, etc. A better understanding of the predictability issues and proper diagnostics and accounting of the model deficiencies can lead to significant improvement in the seasonal forecasts. Statistical postprocessing techniques are the alternative ways to improve the forecasting skill. We already discussed about model averaging and its caveats in section 3c . In our case, the arithmetic mean showed good skill in many cases, but they did not necessarily outperform all

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David A. Lavers, Shaun Harrigan, and Christel Prudhomme

approach. WMO Bulletin , Vol. 69, World Meteorological Organization, Geneva, Switzerland, https://public.wmo.int/en/resources/bulletin/innovating-global-hydrological-prediction-through-earth-system-approach . Hersbach , H. , and Coauthors , 2020 : The ERA5 global reanalysis . Quart. J. Roy. Meteor. Soc. , 146 , 1999 – 2049 , https://doi.org/10.1002/qj.3803 . 10.1002/qj.3803 Hewson , T. D. , and F. M. Pillosu , 2020 : A new low-cost technique improves weather forecasts across the world

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Paul W. Miller and Craig A. Ramseyer

than the ERA5-based method. Because the CFS demonstrated a persistent high bias, any effort to compare the 2015 CFS GDI to the ERA5 dataset necessarily involves a calibration technique like the one performed in Fig. 9 . Instead, computing the percentile rank of the 2015 CFS GDI within the 37-yr reforecast/operational dataset neutralizes the CFS’s high bias by allowing it to equally affect all forecasts in the distribution. Over the eastern Caribbean, the 2015 GDI predicted by the CFS with a 0

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Hisham Eldardiry and Faisal Hossain

technique that has recently gained increasing attention in the reservoir operation literature ( Breckpot et al. 2013 ; Galelli et al. 2014 ). MPC is typically implemented in reservoir operations with a rolling horizon decision approach. This approach updates forecasts and decisions with each time step leading to more reliable operation ( Zhao et al. 2012 ; Wan et al. 2016 ). The steps to derive optimal reservoir operation using MPC are as follows: At each decision time instant t , a control problem

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Mohammadvaghef Ghazvinian, Yu Zhang, and Dong-Jun Seo

distribution (CSGD; Scheuerer and Hamill 2015 ; Baran and Nemoda 2016 ). These techniques represent the discontinuous–continuous nature of precipitation using left-censored distributions and rely on heteroscedastic distributional regression to derive distribution parameters. Unlike logistic regression and its extended version ( Wilks 2009 ), EMOS variants offer the flexibility of incorporating ensemble attributes such as spread, as well as other forecast variables as additional predictors. Perhaps the

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Hernan A. Moreno, Enrique R. Vivoni, and David J. Gochis

. 2004 ; Verbunt et al. 2007 ; Anagnostou et al. 2010 ; Moreno et al. 2012 ) and thus in the use of radar nowcasting techniques for predicting the timing, location, and magnitude of precipitation as input to hydrologic models. Uncertainties inherent in radar nowcasting QPFs are a consequence of the difficulty to forecast rainfall fields for extended periods given that extrapolation functions lose their correlation structures at large lead times (e.g., Sharif et al. 2006 ; Vivoni et al. 2007b

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Rui Mei, Guiling Wang, and Huanghe Gu

appears in the semiarid transition zones between arid and humid regions, including the U.S. Great Plains, among others. Following GLACE1, phase 2 of the project (GLACE2) focuses on quantifying the degree to which realistic land surface initialization contributes to the skill of subseasonal forecasts for precipitation and near-surface air temperature. The skill index as defined in GLACE2 emphasizes the temporal variability rather than the mean climatology of these climate variables concerned. The idea

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Zachary P. Brodeur and Scott Steinschneider

in their characterization ( Dacre et al. 2015 ; Guirguis et al. 2018 ; Hecht and Cordeira 2017 ), classification ( Dettinger et al. 2018 ; Ralph et al. 2019 ), and predictability ( Baggett et al. 2017 ; DeFlorio et al. 2018a , b ; Lavers et al. 2016 , 2017 ). Predictive skill at medium range (1–14 days) and subseasonal to seasonal (S2S; 15–90 days) time scales has become a topic of particular interest because of its relevance to water infrastructure management decisions, such as forecast

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Wade T. Crow, Concepcion Arroyo Gomez, Joaquín Muñoz Sabater, Thomas Holmes, Christopher R. Hain, Fangni Lei, Jianzhi Dong, Joseph G. Alfieri, and Martha C. Anderson

1. Introduction During the growing season, soil moisture (SM) typically controls the partitioning of available energy between sensible and latent heat flux at the soil–atmosphere interface and thereby influences the energetic relationship between the land surface and the lower atmosphere. Furthermore, SM time series contain significant temporal persistence that can be exploited to forecast this relationship out in time. Therefore, the realistic initialization of SM states in the land surface

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